{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GBM Discussion\n",
    "\n",
    "The data here is taken form the Data Hackathon3.x - http://datahack.analyticsvidhya.com/contest/data-hackathon-3x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn import cross_validation, metrics\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "\n",
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline\n",
    "from matplotlib.pylab import rcParams\n",
    "rcParams['figure.figsize'] = 12, 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data:\n",
    "\n",
    "The data has gone through following pre-processing:\n",
    "1. City variable dropped because of too many categories\n",
    "2. DOB converted to Age | DOB dropped\n",
    "3. EMI_Loan_Submitted_Missing created which is 1 if EMI_Loan_Submitted was missing else 0 | EMI_Loan_Submitted dropped\n",
    "4. EmployerName dropped because of too many categories\n",
    "5. Existing_EMI imputed with 0 (median) - 111 values were missing\n",
    "6. Interest_Rate_Missing created which is 1 if Interest_Rate was missing else 0 | Interest_Rate dropped \n",
    "7. Lead_Creation_Date dropped because made little intuitive impact on outcome\n",
    "8. Loan_Amount_Applied, Loan_Tenure_Applied imputed with missing\n",
    "9. Loan_Amount_Submitted_Missing created which is 1 if Loan_Amount_Submitted was missing else 0 | Loan_Amount_Submitted dropped \n",
    "10. Loan_Tenure_Submitted_Missing created which is 1 if Loan_Tenure_Submitted was missing else 0 | Loan_Tenure_Submitted dropped \n",
    "11. LoggedIn, Salary_Account removed\n",
    "12. Processing_Fee_Missing created which is 1 if Processing_Fee was missing else 0 | Processing_Fee dropped\n",
    "13. Source - top 2 kept as is and all others combined into different category\n",
    "14. Numerical and One-Hot-Coding performed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv('train_modified.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "target='Disbursed'\n",
    "IDcol = 'ID'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    85747\n",
       "1     1273\n",
       "Name: Disbursed, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Disbursed'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define a function for modeling and cross-validation\n",
    "\n",
    "This function will do the following:\n",
    "1. fit the model\n",
    "2. determine training accuracy\n",
    "3. determine training AUC\n",
    "4. determine testing AUC\n",
    "5. perform CV is performCV is True\n",
    "6. plot Feature Importance if printFeatureImportance is True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def modelfit(alg, dtrain, dtest, predictors, performCV=True, printFeatureImportance=True, cv_folds=5):\n",
    "    #Fit the algorithm on the data\n",
    "    alg.fit(dtrain[predictors], dtrain['Disbursed'])\n",
    "        \n",
    "    #Predict training set:\n",
    "    dtrain_predictions = alg.predict(dtrain[predictors])\n",
    "    dtrain_predprob = alg.predict_proba(dtrain[predictors])[:,1]\n",
    "    \n",
    "    #Perform cross-validation:\n",
    "    if performCV:\n",
    "        cv_score = cross_validation.cross_val_score(alg, dtrain[predictors], dtrain['Disbursed'], cv=cv_folds, scoring='roc_auc')\n",
    "    \n",
    "    #Print model report:\n",
    "    print \"\\nModel Report\"\n",
    "    print \"Accuracy : %.4g\" % metrics.accuracy_score(dtrain['Disbursed'].values, dtrain_predictions)\n",
    "    print \"AUC Score (Train): %f\" % metrics.roc_auc_score(dtrain['Disbursed'], dtrain_predprob)\n",
    "    \n",
    "    if performCV:\n",
    "        print \"CV Score : Mean - %.7g | Std - %.7g | Min - %.7g | Max - %.7g\" % (np.mean(cv_score),np.std(cv_score),np.min(cv_score),np.max(cv_score))\n",
    "                \n",
    "    #Print Feature Importance:\n",
    "    if printFeatureImportance:\n",
    "        feat_imp = pd.Series(alg.feature_importances_, predictors).sort_values(ascending=False)\n",
    "        feat_imp.plot(kind='bar', title='Feature Importances')\n",
    "        plt.ylabel('Feature Importance Score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Baseline Model\n",
    "\n",
    "Since here the criteria is AUC, simply predicting the most prominent class would give an AUC of 0.5 always. Another way of getting a baseline model is to use the algorithm without tuning, i.e. with default parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report\n",
      "Accuracy : 0.9856\n",
      "AUC Score (Train): 0.862264\n",
      "CV Score : Mean - 0.8319 | Std - 0.008757 | Min - 0.8208 | Max - 0.8439\n"
     ]
    },
    {
     "data": {
      "image/png": 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MzMzMjAqjf0haFzgEeDFwD3ADsHtE3Nh67iry6B9mZmZm1raRTP4iaWlgRkQ8MMrMjYIb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\nqkMIIYQQQhhQDKpDCCGEEEIY0P8HxyTX7ushTo4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1064f4210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Choose all predictors except target & IDcols\n",
    "predictors = [x for x in train.columns if x not in [target, IDcol]]\n",
    "gbm0 = GradientBoostingClassifier(random_state=10)\n",
    "modelfit(gbm0, train, test, predictors,printOOB=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GBM Models:\n",
    "\n",
    "There 2 types of parameters here:\n",
    "1. Tree-specific parameters\n",
    "  * min_samples_split\n",
    "  * min_samples_leaf\n",
    "  * max_depth\n",
    "  * min_leaf_nodes\n",
    "  * max_features\n",
    "  * loss function\n",
    "2. Boosting specific paramters\n",
    "  * n_estimators\n",
    "  * learning_rate\n",
    "  * subsample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Approach for tackling the problem\n",
    "\n",
    "1. Decide a relatively higher value for learning rate and tune the number of estimators requried for that.\n",
    "2. Tune the tree specific parameters for that learning rate\n",
    "3. Tune subsample\n",
    "4. Lower learning rate as much as possible computationally and increase the number of estimators accordingly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1- Find the number of estimators for a high learning rate\n",
    "\n",
    "We will use the following benchmarks for parameters:\n",
    "1. min_samples_split = 500 : ~0.5-1% of total values. Since this is imbalanced class problem, we'll take small value\n",
    "2. min_samples_leaf = 50 : Just using for preventing overfitting. will be tuned later.\n",
    "3. max_depth = 8 : since high number of observations and predictors, choose relatively high value\n",
    "4. max_features = 'sqrt' : general thumbrule to start with\n",
    "5. subsample = 0.8 : typically used value (will be tuned later)\n",
    "\n",
    "0.1 is assumed to be a good learning rate to start with. Let's try to find the optimum number of estimators requried for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=GradientBoostingClassifier(init=None, learning_rate=0.1, loss='deviance',\n",
       "              max_depth=8, max_features='sqrt', max_leaf_nodes=None,\n",
       "              min_samples_leaf=50, min_samples_split=500,\n",
       "              min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "              presort='auto', random_state=10, subsample=0.8, verbose=0,\n",
       "              warm_start=False),\n",
       "       fit_params={}, iid=False, n_jobs=4,\n",
       "       param_grid={'n_estimators': [20, 30, 40, 50, 60, 70, 80]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Choose all predictors except target & IDcols\n",
    "predictors = [x for x in train.columns if x not in [target, IDcol]]\n",
    "param_test1 = {'n_estimators':range(20,81,10)}\n",
    "gsearch1 = GridSearchCV(estimator = GradientBoostingClassifier(learning_rate=0.1, min_samples_split=500,\n",
    "                                  min_samples_leaf=50,max_depth=8,max_features='sqrt', subsample=0.8,random_state=10), \n",
    "                       param_grid = param_test1, scoring='roc_auc',n_jobs=4,iid=False, cv=5)\n",
    "gsearch1.fit(train[predictors],train[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.83322, std: 0.00985, params: {'n_estimators': 20},\n",
       "  mean: 0.83684, std: 0.00986, params: {'n_estimators': 30},\n",
       "  mean: 0.83752, std: 0.00978, params: {'n_estimators': 40},\n",
       "  mean: 0.83761, std: 0.00991, params: {'n_estimators': 50},\n",
       "  mean: 0.83843, std: 0.00987, params: {'n_estimators': 60},\n",
       "  mean: 0.83832, std: 0.00956, params: {'n_estimators': 70},\n",
       "  mean: 0.83764, std: 0.01001, params: {'n_estimators': 80}],\n",
       " {'n_estimators': 60},\n",
       " 0.83842766395593704)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch1.grid_scores_, gsearch1.best_params_, gsearch1.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we got 60 as the optimal estimators for the 0.1 learning rate. Note that 60 is a reasonable value and can be used as it is. But it might not be the same in all cases. Other situations:\n",
    "1. If the value is around 20, you might want to try lowering the learning rate to 0.05 and re-run grid search\n",
    "2. If the values are too high ~100, tuning the other parameters will take long time and you can try a higher learning rate\n",
    "\n",
    "## Step 2- Tune tree-specific parameters\n",
    "Now, lets move onto tuning the tree parameters. We will do this in 2 stages:\n",
    "1. Tune max_depth and num_samples_split\n",
    "2. Tune min_samples_leaf\n",
    "3. Tune max_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=GradientBoostingClassifier(init=None, learning_rate=0.1, loss='deviance',\n",
       "              max_depth=3, max_features='sqrt', max_leaf_nodes=None,\n",
       "              min_samples_leaf=1, min_samples_split=2,\n",
       "              min_weight_fraction_leaf=0.0, n_estimators=60,\n",
       "              presort='auto', random_state=10, subsample=0.8, verbose=0,\n",
       "              warm_start=False),\n",
       "       fit_params={}, iid=False, n_jobs=4,\n",
       "       param_grid={'min_samples_split': [200, 400, 600, 800, 1000], 'max_depth': [5, 7, 9, 11, 13, 15]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grid seach on subsample and max_features\n",
    "param_test2 = {'max_depth':range(5,16,2), 'min_samples_split':range(200,1001,200)}\n",
    "gsearch2 = GridSearchCV(estimator = GradientBoostingClassifier(learning_rate=0.1, n_estimators=60,\n",
    "                                                max_features='sqrt', subsample=0.8, random_state=10), \n",
    "                       param_grid = param_test2, scoring='roc_auc',n_jobs=4,iid=False, cv=5)\n",
    "gsearch2.fit(train[predictors],train[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.83256, std: 0.01272, params: {'min_samples_split': 200, 'max_depth': 5},\n",
       "  mean: 0.83285, std: 0.01016, params: {'min_samples_split': 400, 'max_depth': 5},\n",
       "  mean: 0.83386, std: 0.01415, params: {'min_samples_split': 600, 'max_depth': 5},\n",
       "  mean: 0.83379, std: 0.01169, params: {'min_samples_split': 800, 'max_depth': 5},\n",
       "  mean: 0.83338, std: 0.01268, params: {'min_samples_split': 1000, 'max_depth': 5},\n",
       "  mean: 0.83390, std: 0.00759, params: {'min_samples_split': 200, 'max_depth': 7},\n",
       "  mean: 0.83660, std: 0.00994, params: {'min_samples_split': 400, 'max_depth': 7},\n",
       "  mean: 0.83481, std: 0.00827, params: {'min_samples_split': 600, 'max_depth': 7},\n",
       "  mean: 0.83788, std: 0.01066, params: {'min_samples_split': 800, 'max_depth': 7},\n",
       "  mean: 0.83769, std: 0.01060, params: {'min_samples_split': 1000, 'max_depth': 7},\n",
       "  mean: 0.83631, std: 0.00942, params: {'min_samples_split': 200, 'max_depth': 9},\n",
       "  mean: 0.83695, std: 0.00923, params: {'min_samples_split': 400, 'max_depth': 9},\n",
       "  mean: 0.83339, std: 0.00893, params: {'min_samples_split': 600, 'max_depth': 9},\n",
       "  mean: 0.83793, std: 0.00965, params: {'min_samples_split': 800, 'max_depth': 9},\n",
       "  mean: 0.83844, std: 0.00954, params: {'min_samples_split': 1000, 'max_depth': 9},\n",
       "  mean: 0.83036, std: 0.00998, params: {'min_samples_split': 200, 'max_depth': 11},\n",
       "  mean: 0.83077, std: 0.00809, params: {'min_samples_split': 400, 'max_depth': 11},\n",
       "  mean: 0.83366, std: 0.00983, params: {'min_samples_split': 600, 'max_depth': 11},\n",
       "  mean: 0.83193, std: 0.00911, params: {'min_samples_split': 800, 'max_depth': 11},\n",
       "  mean: 0.83582, std: 0.01040, params: {'min_samples_split': 1000, 'max_depth': 11},\n",
       "  mean: 0.82198, std: 0.01037, params: {'min_samples_split': 200, 'max_depth': 13},\n",
       "  mean: 0.83055, std: 0.00837, params: {'min_samples_split': 400, 'max_depth': 13},\n",
       "  mean: 0.83139, std: 0.01127, params: {'min_samples_split': 600, 'max_depth': 13},\n",
       "  mean: 0.83403, std: 0.01060, params: {'min_samples_split': 800, 'max_depth': 13},\n",
       "  mean: 0.83288, std: 0.00974, params: {'min_samples_split': 1000, 'max_depth': 13},\n",
       "  mean: 0.82009, std: 0.00691, params: {'min_samples_split': 200, 'max_depth': 15},\n",
       "  mean: 0.82317, std: 0.01017, params: {'min_samples_split': 400, 'max_depth': 15},\n",
       "  mean: 0.82909, std: 0.00904, params: {'min_samples_split': 600, 'max_depth': 15},\n",
       "  mean: 0.82926, std: 0.00944, params: {'min_samples_split': 800, 'max_depth': 15},\n",
       "  mean: 0.83236, std: 0.01421, params: {'min_samples_split': 1000, 'max_depth': 15}],\n",
       " {'max_depth': 9, 'min_samples_split': 1000},\n",
       " 0.83843938077464664)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch2.grid_scores_, gsearch2.best_params_, gsearch2.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we reached the maximum of min_sales_split, we should check higher values as well. Also, we can tune min_samples_leaf with it now as max_depth is fixed. One might argue that max depth might change for higher value but if you observe the output closely, a max_depth of 9 had a better model for most of cases.\n",
    "So lets perform a grid search on them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=GradientBoostingClassifier(init=None, learning_rate=0.1, loss='deviance',\n",
       "              max_depth=9, max_features='sqrt', max_leaf_nodes=None,\n",
       "              min_samples_leaf=1, min_samples_split=2,\n",
       "              min_weight_fraction_leaf=0.0, n_estimators=60,\n",
       "              presort='auto', random_state=10, subsample=0.8, verbose=0,\n",
       "              warm_start=False),\n",
       "       fit_params={}, iid=False, n_jobs=4,\n",
       "       param_grid={'min_samples_split': [1000, 1200, 1400, 1600, 1800, 2000], 'min_samples_leaf': [30, 40, 50, 60, 70]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grid seach on subsample and max_features\n",
    "param_test3 = {'min_samples_split':range(1000,2100,200), 'min_samples_leaf':range(30,71,10)}\n",
    "gsearch3 = GridSearchCV(estimator = GradientBoostingClassifier(learning_rate=0.1, n_estimators=60,max_depth=9,\n",
    "                                                    max_features='sqrt', subsample=0.8, random_state=10), \n",
    "                       param_grid = param_test3, scoring='roc_auc',n_jobs=4,iid=False, cv=5)\n",
    "gsearch3.fit(train[predictors],train[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.83821, std: 0.01092, params: {'min_samples_split': 1000, 'min_samples_leaf': 30},\n",
       "  mean: 0.83889, std: 0.01271, params: {'min_samples_split': 1200, 'min_samples_leaf': 30},\n",
       "  mean: 0.83552, std: 0.01024, params: {'min_samples_split': 1400, 'min_samples_leaf': 30},\n",
       "  mean: 0.83683, std: 0.01429, params: {'min_samples_split': 1600, 'min_samples_leaf': 30},\n",
       "  mean: 0.83958, std: 0.01233, params: {'min_samples_split': 1800, 'min_samples_leaf': 30},\n",
       "  mean: 0.83783, std: 0.01137, params: {'min_samples_split': 2000, 'min_samples_leaf': 30},\n",
       "  mean: 0.83821, std: 0.00872, params: {'min_samples_split': 1000, 'min_samples_leaf': 40},\n",
       "  mean: 0.83740, std: 0.01280, params: {'min_samples_split': 1200, 'min_samples_leaf': 40},\n",
       "  mean: 0.83714, std: 0.01019, params: {'min_samples_split': 1400, 'min_samples_leaf': 40},\n",
       "  mean: 0.83771, std: 0.01188, params: {'min_samples_split': 1600, 'min_samples_leaf': 40},\n",
       "  mean: 0.83738, std: 0.01370, params: {'min_samples_split': 1800, 'min_samples_leaf': 40},\n",
       "  mean: 0.83765, std: 0.01221, params: {'min_samples_split': 2000, 'min_samples_leaf': 40},\n",
       "  mean: 0.83575, std: 0.01017, params: {'min_samples_split': 1000, 'min_samples_leaf': 50},\n",
       "  mean: 0.83744, std: 0.01224, params: {'min_samples_split': 1200, 'min_samples_leaf': 50},\n",
       "  mean: 0.83892, std: 0.01234, params: {'min_samples_split': 1400, 'min_samples_leaf': 50},\n",
       "  mean: 0.83814, std: 0.01354, params: {'min_samples_split': 1600, 'min_samples_leaf': 50},\n",
       "  mean: 0.83824, std: 0.01116, params: {'min_samples_split': 1800, 'min_samples_leaf': 50},\n",
       "  mean: 0.83821, std: 0.01014, params: {'min_samples_split': 2000, 'min_samples_leaf': 50},\n",
       "  mean: 0.83626, std: 0.01111, params: {'min_samples_split': 1000, 'min_samples_leaf': 60},\n",
       "  mean: 0.83959, std: 0.00989, params: {'min_samples_split': 1200, 'min_samples_leaf': 60},\n",
       "  mean: 0.83735, std: 0.01217, params: {'min_samples_split': 1400, 'min_samples_leaf': 60},\n",
       "  mean: 0.83685, std: 0.01325, params: {'min_samples_split': 1600, 'min_samples_leaf': 60},\n",
       "  mean: 0.83589, std: 0.01101, params: {'min_samples_split': 1800, 'min_samples_leaf': 60},\n",
       "  mean: 0.83769, std: 0.01173, params: {'min_samples_split': 2000, 'min_samples_leaf': 60},\n",
       "  mean: 0.83792, std: 0.00994, params: {'min_samples_split': 1000, 'min_samples_leaf': 70},\n",
       "  mean: 0.83712, std: 0.01053, params: {'min_samples_split': 1200, 'min_samples_leaf': 70},\n",
       "  mean: 0.83777, std: 0.01186, params: {'min_samples_split': 1400, 'min_samples_leaf': 70},\n",
       "  mean: 0.83812, std: 0.01126, params: {'min_samples_split': 1600, 'min_samples_leaf': 70},\n",
       "  mean: 0.83812, std: 0.01055, params: {'min_samples_split': 1800, 'min_samples_leaf': 70},\n",
       "  mean: 0.83677, std: 0.01190, params: {'min_samples_split': 2000, 'min_samples_leaf': 70}],\n",
       " {'min_samples_leaf': 60, 'min_samples_split': 1200},\n",
       " 0.83959087132827259)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch3.grid_scores_, gsearch3.best_params_, gsearch3.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report\n",
      "Accuracy : 0.9854\n",
      "AUC Score (Train): 0.896453\n",
      "CV Score : Mean - 0.8395909 | Std - 0.009890497 | Min - 0.8259075 | Max - 0.8527672\n"
     ]
    },
    {
     "data": {
      "image/png": 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XPD8B3gwgaQvgrohYUDHWzMzMzGzKtXqlOiIekvQ+4FRSBf7QiLhS0l7p4zg4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10df94ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "modelfit(gsearch3.best_estimator_, train, test, predictors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tune max_features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=GradientBoostingClassifier(init=None, learning_rate=0.1, loss='deviance',\n",
       "              max_depth=9, max_features=None, max_leaf_nodes=None,\n",
       "              min_samples_leaf=60, min_samples_split=1200,\n",
       "              min_weight_fraction_leaf=0.0, n_estimators=60,\n",
       "              presort='auto', random_state=10, subsample=0.8, verbose=0,\n",
       "              warm_start=False),\n",
       "       fit_params={}, iid=False, n_jobs=4,\n",
       "       param_grid={'max_features': [7, 9, 11, 13, 15, 17, 19]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grid seach on subsample and max_features\n",
    "param_test4 = {'max_features':range(7,20,2)}\n",
    "gsearch4 = GridSearchCV(estimator = GradientBoostingClassifier(learning_rate=0.1, n_estimators=60,max_depth=9, \n",
    "                            min_samples_split=1200, min_samples_leaf=60, subsample=0.8, random_state=10),\n",
    "                       param_grid = param_test4, scoring='roc_auc',n_jobs=4,iid=False, cv=5)\n",
    "gsearch4.fit(train[predictors],train[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.83959, std: 0.00989, params: {'max_features': 7},\n",
       "  mean: 0.83648, std: 0.00988, params: {'max_features': 9},\n",
       "  mean: 0.83919, std: 0.01042, params: {'max_features': 11},\n",
       "  mean: 0.83738, std: 0.01017, params: {'max_features': 13},\n",
       "  mean: 0.83820, std: 0.01017, params: {'max_features': 15},\n",
       "  mean: 0.83495, std: 0.00957, params: {'max_features': 17},\n",
       "  mean: 0.83499, std: 0.00996, params: {'max_features': 19}],\n",
       " {'max_features': 7},\n",
       " 0.83959087132827259)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch4.grid_scores_, gsearch4.best_params_, gsearch4.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step3- Tune Subsample and Lower Learning Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=GradientBoostingClassifier(init=None, learning_rate=0.1, loss='deviance',\n",
       "              max_depth=9, max_features=7, max_leaf_nodes=None,\n",
       "              min_samples_leaf=60, min_samples_split=1200,\n",
       "              min_weight_fraction_leaf=0.0, n_estimators=60,\n",
       "              presort='auto', random_state=10, subsample=0.8, verbose=0,\n",
       "              warm_start=False),\n",
       "       fit_params={}, iid=False, n_jobs=4,\n",
       "       param_grid={'subsample': [0.6, 0.7, 0.75, 0.8, 0.85, 0.9]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grid seach on subsample and max_features\n",
    "param_test5 = {'subsample':[0.6,0.7,0.75,0.8,0.85,0.9]}\n",
    "gsearch5 = GridSearchCV(estimator = GradientBoostingClassifier(learning_rate=0.1, n_estimators=60,max_depth=9, \n",
    "                            min_samples_split=1200, min_samples_leaf=60, subsample=0.8, random_state=10, max_features=7),\n",
    "                       param_grid = param_test5, scoring='roc_auc',n_jobs=4,iid=False, cv=5)\n",
    "gsearch5.fit(train[predictors],train[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.83621, std: 0.00950, params: {'subsample': 0.6},\n",
       "  mean: 0.83648, std: 0.01181, params: {'subsample': 0.7},\n",
       "  mean: 0.83601, std: 0.01074, params: {'subsample': 0.75},\n",
       "  mean: 0.83959, std: 0.00989, params: {'subsample': 0.8},\n",
       "  mean: 0.83989, std: 0.01078, params: {'subsample': 0.85},\n",
       "  mean: 0.83827, std: 0.01076, params: {'subsample': 0.9}],\n",
       " {'subsample': 0.85},\n",
       " 0.83988852960292915)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch5.grid_scores_, gsearch5.best_params_, gsearch5.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With all tuned lets try reducing the learning rate and proportionally increasing the number of estimators to get more robust results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report\n",
      "Accuracy : 0.9854\n",
      "AUC Score (Train): 0.897471\n",
      "CV Score : Mean - 0.8396 | Std - 0.009514 | Min - 0.8266 | Max - 0.8516\n"
     ]
    },
    {
     "data": {
      "image/png": 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bJa1KKlxfExHnjihuMzMzM7NSyhSqjwbOkvS9PP1W4IiS678NWLswvVaeV0pE3J7//lXS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\nLx0RveoTLULSkaQqFb8gPZ0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r1cBx5XDnAB8jFQxmk94c9KsfOtC+JtVv3orUw8ixpHYd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\nhBBmKAbVIYQQQgghzND/AaM2J/8LzsENAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e8f1850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Choose all predictors except target & IDcols\n",
    "predictors = [x for x in train.columns if x not in [target, IDcol]]\n",
    "gbm_tuned_1 = GradientBoostingClassifier(learning_rate=0.05, n_estimators=120,max_depth=9, min_samples_split=1200, \n",
    "                                         min_samples_leaf=60, subsample=0.85, random_state=10, max_features=7)\n",
    "modelfit(gbm_tuned_1, train, test, predictors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1/10th learning rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report\n",
      "Accuracy : 0.9854\n",
      "AUC Score (Train): 0.899927\n",
      "CV Score : Mean - 0.8409339 | Std - 0.01035658 | Min - 0.8258238 | Max - 0.8529458\n"
     ]
    },
    {
     "data": {
      "image/png": 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JDfjDI+IaSfumh+PQiPi5pJ0k/QF4EHhrv9g28zUzMzMzq6PVPtVmZmZmZo8F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\n6hBCCCGEEKbo/wDlCGddRcnqVAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e2200d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Choose all predictors except target & IDcols\n",
    "predictors = [x for x in train.columns if x not in [target, IDcol]]\n",
    "gbm_tuned_2 = GradientBoostingClassifier(learning_rate=0.01, n_estimators=600,max_depth=9, min_samples_split=1200, \n",
    "                                         min_samples_leaf=60, subsample=0.85, random_state=10, max_features=7)\n",
    "modelfit(gbm_tuned_2, train, test, predictors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1/50th learning rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report\n",
      "Accuracy : 0.9854\n",
      "AUC Score (Train): 0.900688\n"
     ]
    },
    {
     "data": {
      "image/png": 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jzKvV/OqW/QSesyqnmcB9az3Guvk1TFMp3XStVC8ZEf3a6ixE0lGk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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111401650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Choose all predictors except target & IDcols\n",
    "predictors = [x for x in train.columns if x not in [target, IDcol]]\n",
    "gbm_tuned_3 = GradientBoostingClassifier(learning_rate=0.005, n_estimators=1200,max_depth=9, min_samples_split=1200, \n",
    "                                         min_samples_leaf=60, subsample=0.85, random_state=10, max_features=7,\n",
    "                                         warm_start=True)\n",
    "modelfit(gbm_tuned_3, train, test, predictors, performCV=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report\n",
      "Accuracy : 0.9854\n",
      "AUC Score (Train): 0.906346\n"
     ]
    },
    {
     "data": {
      "image/png": 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IuGBEZZuZmZmZVVKlUX0McK6kI/L9twNHVtz+HcAahfur52WVRMSd+e+fJZ1C6k7StVF9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\nM24I7+vJcAxpPUfXY7O4qdq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dVo6ru9/G4fU4ks/CBvXhehxSXPE2JSd/Ic3Y9vzI41wq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e2cf710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Choose all predictors except target & IDcols\n",
    "predictors = [x for x in train.columns if x not in [target, IDcol]]\n",
    "gbm_tuned_4 = GradientBoostingClassifier(learning_rate=0.005, n_estimators=1500,max_depth=9, min_samples_split=1200, \n",
    "                                         min_samples_leaf=60, subsample=0.85, random_state=10, max_features=7,\n",
    "                                         warm_start=True)\n",
    "modelfit(gbm_tuned_4, train, test, predictors, performCV=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
